Project Summary Multiple sclerosis (MS) is two to three fold more prevalent in Caucasian Americans (CA) than in African Americans (AA). However, disease severity is significantly greater in African Americans as judged by clinical feature such as greater ambulatory disability, mean time to diagnosis after first symptoms and lower gray matter volume. While the proportion of AA RRMS to CA RRMS is greater at the University of Alabama at Birmingham MS Clinics, features such as mean age at first diagnosis, relapse rate and age at symptoms were first observed compare to other studies. The underlying mechanism for the differential disease severity and progression between AA and CA is unknown. The goal of this proposal is to fill this critical gap in our knowledge. Our preliminary data reveals heterogeneity in T cell populations between AA and CA newly diagnosed treatment-nave RRMS patients. We also observed that certain subpopulations of CD4 and CD8 T cells from AA RRMS patients responded more vigorously to IL-2 than CA RRMS patients. Notably, response to IFN-? in all CD4 and CD8 T cell populations was similar in AA and CA RMS. From these preliminary data, and published knowledge, we hypothesize that functional heterogeneity in specific adaptive (CD4 and CD8) immune cell populations contribute to the more severe relapsing remitting multiple sclerosis (RRMS) pathogenesis in AA. To elucidate the functional differences in an unbiased manner, we propose to perform high-depth single cell whole genome RNAseq (scRNAseq) of peripheral blood cells integrated with epitope- based indexing of cell subsets. The strategy is designed to obtain gene expression information at the single level and facilitate cluster analysis with knowledge of cell phenotype. This will allow us to interrogate heterogeneity within cell between AA and CA from which mechanistic studies will be performed. For this proposal, all studies will be performed using peripheral blood cells from newly diagnosed pre-treatment AA and CA RRMS patients with potential for longitudinal data. Functional studies will complement the RNAseq data. We will utilize advanced hierarchical computational approaches for unbiased data analysis that will enable identification of rare events. The primary goal we expect to achieve from this explorative study is the discovery of mechanisms that contribute to RRMS disease severity in AA. Additional outcome is identification of biomarkers that might predict treatment response and disease progression in both AA and CA RRMS.